





from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


snake_case								=							logging.get_logger(__name__)

# General docstring
snake_case								=							"""ResNetConfig"""

# Base docstring
snake_case								=							"""microsoft/resnet-50"""
snake_case								=							[1, 2_0_4_8, 7, 7]

# Image classification docstring
snake_case								=							"""microsoft/resnet-50"""
snake_case								=							"""tiger cat"""

snake_case								=							[
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]





class 					A_					(   nn.Module						):







       """simple docstring"""





       def __init__(     self						:						Tuple    ,__A						:						int    ,__A						:						int    ,__A						:						int = 3    ,__A						:						int = 1    ,__A						:						str = "relu"	)    ->       str:
         super().__init__()
         _lowercase						    =			nn.Convad(
             __A    ,__A    ,kernel_size=__A    ,stride=__A    ,padding=kernel_size // 2    ,bias=__A	)
         _lowercase						    =			nn.BatchNormad(__A	)
         _lowercase						    =			ACTaFN[activation] if activation is not None else nn.Identity()







       def  __UpperCAmelCase			(     self						:						Tuple    ,__A						:						Tensor	)    ->       Tensor:
         _lowercase						    =			self.convolution(__A	)
         _lowercase						    =			self.normalization(__A	)
         _lowercase						    =			self.activation(__A	)
         return hidden_state





class 					A_					(   nn.Module						):







       """simple docstring"""





       def __init__(     self						:						Dict    ,__A						:						ResNetConfig	)    ->       Tuple:
         super().__init__()
         _lowercase						    =			ResNetConvLayer(
             config.num_channels    ,config.embedding_size    ,kernel_size=7    ,stride=2    ,activation=config.hidden_act	)
         _lowercase						    =			nn.MaxPoolad(kernel_size=3    ,stride=2    ,padding=1	)
         _lowercase						    =			config.num_channels







       def  __UpperCAmelCase			(     self						:						int    ,__A						:						Tensor	)    ->       Tensor:
         _lowercase						    =			pixel_values.shape[1]
         if num_channels != self.num_channels:
           raise ValueError(
               'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'	)
         _lowercase						    =			self.embedder(__A	)
         _lowercase						    =			self.pooler(__A	)
         return embedding





class 					A_					(   nn.Module						):







       """simple docstring"""





       def __init__(     self						:						int    ,__A						:						int    ,__A						:						int    ,__A						:						int = 2	)    ->       int:
         super().__init__()
         _lowercase						    =			nn.Convad(__A    ,__A    ,kernel_size=1    ,stride=__A    ,bias=__A	)
         _lowercase						    =			nn.BatchNormad(__A	)







       def  __UpperCAmelCase			(     self						:						Union[str, Any]    ,__A						:						Tensor	)    ->       Tensor:
         _lowercase						    =			self.convolution(__A	)
         _lowercase						    =			self.normalization(__A	)
         return hidden_state





class 					A_					(   nn.Module						):







       """simple docstring"""





       def __init__(     self						:						int    ,__A						:						int    ,__A						:						int    ,__A						:						int = 1    ,__A						:						str = "relu"	)    ->       Any:
         super().__init__()
         _lowercase						    =			in_channels != out_channels or stride != 1
         _lowercase						    =			(
             ResNetShortCut(__A    ,__A    ,stride=__A	) if should_apply_shortcut else nn.Identity()
         )
         _lowercase						    =			nn.Sequential(
             ResNetConvLayer(__A    ,__A    ,stride=__A	)    ,ResNetConvLayer(__A    ,__A    ,activation=__A	)    ,)
         _lowercase						    =			ACTaFN[activation]







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						Dict	)    ->       Any:
         _lowercase						    =			hidden_state
         _lowercase						    =			self.layer(__A	)
         _lowercase						    =			self.shortcut(__A	)
         hidden_state += residual
         _lowercase						    =			self.activation(__A	)
         return hidden_state





class 					A_					(   nn.Module						):







       """simple docstring"""





       def __init__(     self						:						Union[str, Any]    ,__A						:						int    ,__A						:						int    ,__A						:						int = 1    ,__A						:						str = "relu"    ,__A						:						int = 4	)    ->       List[str]:
         super().__init__()
         _lowercase						    =			in_channels != out_channels or stride != 1
         _lowercase						    =			out_channels // reduction
         _lowercase						    =			(
             ResNetShortCut(__A    ,__A    ,stride=__A	) if should_apply_shortcut else nn.Identity()
         )
         _lowercase						    =			nn.Sequential(
             ResNetConvLayer(__A    ,__A    ,kernel_size=1	)    ,ResNetConvLayer(__A    ,__A    ,stride=__A	)    ,ResNetConvLayer(__A    ,__A    ,kernel_size=1    ,activation=__A	)    ,)
         _lowercase						    =			ACTaFN[activation]







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						List[str]	)    ->       Any:
         _lowercase						    =			hidden_state
         _lowercase						    =			self.layer(__A	)
         _lowercase						    =			self.shortcut(__A	)
         hidden_state += residual
         _lowercase						    =			self.activation(__A	)
         return hidden_state





class 					A_					(   nn.Module						):







       """simple docstring"""





       def __init__(     self						:						Any    ,__A						:						ResNetConfig    ,__A						:						int    ,__A						:						int    ,__A						:						int = 2    ,__A						:						int = 2    ,)    ->       List[str]:
         super().__init__()

         _lowercase						    =			ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

         _lowercase						    =			nn.Sequential(
             # downsampling is done in the first layer with stride of 2
             layer(__A    ,__A    ,stride=__A    ,activation=config.hidden_act	)    ,*[layer(__A    ,__A    ,activation=config.hidden_act	) for _ in range(depth - 1	)]    ,)







       def  __UpperCAmelCase			(     self						:						Union[str, Any]    ,__A						:						Tensor	)    ->       Tensor:
         _lowercase						    =			input
         for layer in self.layers:
           _lowercase						    =			layer(__A	)
         return hidden_state





class 					A_					(   nn.Module						):







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						ResNetConfig	)    ->       Union[str, Any]:
         super().__init__()
         _lowercase						    =			nn.ModuleList([]	)
         # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
         self.stages.append(
             ResNetStage(
                 __A    ,config.embedding_size    ,config.hidden_sizes[0]    ,stride=2 if config.downsample_in_first_stage else 1    ,depth=config.depths[0]    ,)	)
         _lowercase						    =			zip(config.hidden_sizes    ,config.hidden_sizes[1:]	)
         for (in_channels, out_channels), depth in zip(__A    ,config.depths[1:]	):
           self.stages.append(ResNetStage(__A    ,__A    ,__A    ,depth=__A	)	)







       def  __UpperCAmelCase			(     self						:						Optional[int]    ,__A						:						Tensor    ,__A						:						bool = False    ,__A						:						bool = True	)    ->       BaseModelOutputWithNoAttention:
         _lowercase						    =			() if output_hidden_states else None

         for stage_module in self.stages:
           if output_hidden_states:
             _lowercase						    =			hidden_states + (hidden_state,)

           _lowercase						    =			stage_module(__A	)

         if output_hidden_states:
           _lowercase						    =			hidden_states + (hidden_state,)

         if not return_dict:
           return tuple(v for v in [hidden_state, hidden_states] if v is not None	)

         return BaseModelOutputWithNoAttention(
             last_hidden_state=__A    ,hidden_states=__A    ,)





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			int  			=	ResNetConfig
       SCREAMING_SNAKE_CASE_   :			Any  			=	'''resnet'''
       SCREAMING_SNAKE_CASE_   :			Tuple  			=	'''pixel_values'''
       SCREAMING_SNAKE_CASE_   :			str  			=	True







       def  __UpperCAmelCase			(     self						:						Tuple    ,__A						:						int	)    ->       int:
         if isinstance(__A    ,nn.Convad	):
           nn.init.kaiming_normal_(module.weight    ,mode='fan_out'    ,nonlinearity='relu'	)
         elif isinstance(__A    ,(nn.BatchNormad, nn.GroupNorm)	):
           nn.init.constant_(module.weight    ,1	)
           nn.init.constant_(module.bias    ,0	)







       def  __UpperCAmelCase			(     self						:						Any    ,__A						:						List[Any]    ,__A						:						Union[str, Any]=False	)    ->       Optional[Any]:
         if isinstance(__A    ,__A	):
           _lowercase						    =			value
snake_case								=							R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

snake_case								=							R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""





@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''       ,						UpperCAmelCase       ,						)
class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       def __init__(     self						:						Tuple    ,__A						:						List[Any]	)    ->       Dict:
         super().__init__(__A	)
         _lowercase						    =			config
         _lowercase						    =			ResNetEmbeddings(__A	)
         _lowercase						    =			ResNetEncoder(__A	)
         _lowercase						    =			nn.AdaptiveAvgPoolad((1, 1)	)
         # Initialize weights and apply final processing
         self.post_init()







       @add_start_docstrings_to_model_forward(__A	)
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC    ,output_type=__A    ,config_class=_CONFIG_FOR_DOC    ,modality='vision'    ,expected_output=_EXPECTED_OUTPUT_SHAPE    ,)
       def  __UpperCAmelCase			(     self						:						int    ,__A						:						Tensor    ,__A						:						Optional[bool] = None    ,__A						:						Optional[bool] = None	)    ->       BaseModelOutputWithPoolingAndNoAttention:
         _lowercase						    =			(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         _lowercase						    =			return_dict if return_dict is not None else self.config.use_return_dict

         _lowercase						    =			self.embedder(__A	)

         _lowercase						    =			self.encoder(
             __A    ,output_hidden_states=__A    ,return_dict=__A	)

         _lowercase						    =			encoder_outputs[0]

         _lowercase						    =			self.pooler(__A	)

         if not return_dict:
           return (last_hidden_state, pooled_output) + encoder_outputs[1:]

         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=__A    ,pooler_output=__A    ,hidden_states=encoder_outputs.hidden_states    ,)





@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''       ,						UpperCAmelCase       ,						)
class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						Optional[int]	)    ->       int:
         super().__init__(__A	)
         _lowercase						    =			config.num_labels
         _lowercase						    =			ResNetModel(__A	)
         # classification head
         _lowercase						    =			nn.Sequential(
             nn.Flatten()    ,nn.Linear(config.hidden_sizes[-1]    ,config.num_labels	) if config.num_labels > 0 else nn.Identity()    ,)
         # initialize weights and apply final processing
         self.post_init()







       @add_start_docstrings_to_model_forward(__A	)
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT    ,output_type=__A    ,config_class=_CONFIG_FOR_DOC    ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,)
       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						Optional[torch.FloatTensor] = None    ,__A						:						Optional[torch.LongTensor] = None    ,__A						:						Optional[bool] = None    ,__A						:						Optional[bool] = None    ,)    ->       ImageClassifierOutputWithNoAttention:
         _lowercase						    =			return_dict if return_dict is not None else self.config.use_return_dict

         _lowercase						    =			self.resnet(__A    ,output_hidden_states=__A    ,return_dict=__A	)

         _lowercase						    =			outputs.pooler_output if return_dict else outputs[1]

         _lowercase						    =			self.classifier(__A	)

         _lowercase						    =			None

         if labels is not None:
           if self.config.problem_type is None:
             if self.num_labels == 1:
               _lowercase						    =			'regression'
             elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
               _lowercase						    =			'single_label_classification'
             else:
               _lowercase						    =			'multi_label_classification'
           if self.config.problem_type == "regression":
             _lowercase						    =			MSELoss()
             if self.num_labels == 1:
               _lowercase						    =			loss_fct(logits.squeeze()    ,labels.squeeze()	)
             else:
               _lowercase						    =			loss_fct(__A    ,__A	)
           elif self.config.problem_type == "single_label_classification":
             _lowercase						    =			CrossEntropyLoss()
             _lowercase						    =			loss_fct(logits.view(-1    ,self.num_labels	)    ,labels.view(-1	)	)
           elif self.config.problem_type == "multi_label_classification":
             _lowercase						    =			BCEWithLogitsLoss()
             _lowercase						    =			loss_fct(__A    ,__A	)

         if not return_dict:
           _lowercase						    =			(logits,) + outputs[2:]
           return (loss,) + output if loss is not None else output

         return ImageClassifierOutputWithNoAttention(loss=__A    ,logits=__A    ,hidden_states=outputs.hidden_states	)





@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''       ,						UpperCAmelCase       ,						)
class 					A_					(   UpperCAmelCase       ,						UpperCAmelCase						):







       """simple docstring"""





       def __init__(     self						:						Optional[Any]    ,__A						:						Any	)    ->       List[str]:
         super().__init__(__A	)
         super()._init_backbone(__A	)

         _lowercase						    =			[config.embedding_size] + config.hidden_sizes
         _lowercase						    =			ResNetEmbeddings(__A	)
         _lowercase						    =			ResNetEncoder(__A	)

         # initialize weights and apply final processing
         self.post_init()







       @add_start_docstrings_to_model_forward(__A	)
       @replace_return_docstrings(output_type=__A    ,config_class=_CONFIG_FOR_DOC	)
       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						Tensor    ,__A						:						Optional[bool] = None    ,__A						:						Optional[bool] = None	)    ->       BackboneOutput:
         _lowercase						    =			return_dict if return_dict is not None else self.config.use_return_dict
         _lowercase						    =			(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )

         _lowercase						    =			self.embedder(__A	)

         _lowercase						    =			self.encoder(__A    ,output_hidden_states=__A    ,return_dict=__A	)

         _lowercase						    =			outputs.hidden_states

         _lowercase						    =			()
         for idx, stage in enumerate(self.stage_names	):
           if stage in self.out_features:
             feature_maps += (hidden_states[idx],)

         if not return_dict:
           _lowercase						    =			(feature_maps,)
           if output_hidden_states:
             output += (outputs.hidden_states,)
           return output

         return BackboneOutput(
             feature_maps=__A    ,hidden_states=outputs.hidden_states if output_hidden_states else None    ,attentions=__A    ,)







"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort




@nightly
@require_onnxruntime
@require_torch_gpu
class 						__UpperCamelCase     (     unittest.TestCase						):
					@property
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Dict:
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)
					@property
					def        UpperCAmelCase__ (							self							:	int   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			ort.SessionOptions()
											lowerCAmelCase   :Dict						=			False
											return options
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       str:
											lowerCAmelCase   :Union[str, Any]						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo.png'   )
											lowerCAmelCase   :str						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'   )
											lowerCAmelCase   :List[Any]						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'   )

											# using the PNDM scheduler by default
											lowerCAmelCase   :Tuple						=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'     , revision='onnx'     , safety_checker=UpperCAmelCase     , feature_extractor=UpperCAmelCase     , provider=self.gpu_provider     , sess_options=self.gpu_options     , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			'A red cat sitting on a park bench'

											lowerCAmelCase   :Optional[Any]						=			np.random.RandomState(0   )
											lowerCAmelCase   :Union[str, Any]						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , strength=0.7_5     , guidance_scale=7.5     , num_inference_steps=15     , generator=UpperCAmelCase     , output_type='np'     , )

											lowerCAmelCase   :Optional[int]						=			output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image   ).max() < 1e-2